Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data

Abstract

AbstractTo discover rare disease-gene associations, we developed a gene burden analytical framework and applied it to rare, protein-coding variants from whole genome sequencing of 35,008 cases with rare diseases and their family members recruited to the 100,000 Genomes Project (100KGP). Followingin silicotriaging of the results, 88 novel associations were identified including 38 with existing experimental evidence. We have published the confirmation of one of these associations, hereditary ataxia withUCHL1, and independent confirmatory evidence has recently been published for four more. We highlight a further seven compelling associations: hypertrophic cardiomyopathy withDYSFandSLC4A3where both genes show high/specific heart expression and existing associations to skeletal dystrophies or short QT syndrome respectively; monogenic diabetes withUNC13Awith a known role in the regulation of β cells and a mouse model with impaired glucose tolerance; epilepsy withKCNQ1where a mouse model shows seizures and the existing long QT syndrome association may be linked; early onset Parkinson’s disease withRYR1with existing links to tremor pathophysiology and a mouse model with neurological phenotypes; anterior segment ocular abnormalities associated withPOMKshowing expression in corneal cells and with a zebrafish model with developmental ocular abnormalities; and cystic kidney disease withCOL4A3showing high renal expression and prior evidence for a digenic or modifying role in renal disease. Confirmation of all 88 associations would lead to potential diagnoses in 456 molecularly undiagnosed cases within the 100KGP, as well as other rare disease patients worldwide, highlighting the clinical impact of a large-scale statistical approach to rare disease gene discovery.